| Article | |
| Title | NA61/SHINE online noise filtering using machine learning methods |
| Author(s) | Kawęcka, Anna (Warsaw U. of Tech. (main) ; Chalmers U. Tech.) ; Bryliński, Wojciech (Warsaw U. of Tech. (main)) ; Omana Kuttan, Manjunath (Frankfurt U., FIAS ; Frankfurt U.) ; Linnyk, Olena (Frankfurt U., FIAS ; Giessen U.) ; Pawlowski, Janik (Frankfurt U., FIAS ; Philipps U. Marburg) ; Schmidt, Katarzyna (Silesia U.) ; Słodkowski, Marcin (Warsaw U. of Tech. (main)) ; Wyszyński, Oskar (Jan Kochanowski U., Kielce (main)) ; Zieliński, Jakub (Warsaw U. of Tech. (main)) |
| Publication | 2023 |
| Number of pages | 5 |
| In: | J. Phys. : Conf. Ser. 2438 (2023) 012104 |
| In: | 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021), Daejeon, Korea, 29 Nov - 3 Dec 2021, pp.012104 |
| DOI | 10.1088/1742-6596/2438/1/012104 |
| Subject category | Detectors and Experimental Techniques ; Computing and Computers |
| Accelerator/Facility, Experiment | CERN SPS ; NA61 |
| Abstract | The NA61/SHINE is a high-energy physics experiment operating at the SPS accelerator at CERN. The physics program of the experiment was recently extended, requiring a significant upgrade of the detector setup. The main goal of the upgrade is to increase the event flow rate from 80Hz to 1kHz by exchanging the read-out electronics of the NA61/SHINE main tracking detectors (Time-Projection-Chambers - TPCs). As the amount of collected data will increase significantly, a tool for online noise filtering is needed. The standard method is based on the reconstruction of tracks and removal of clusters which do not belong to any particle trajectory. However, this method takes a substantial amount of time and resources. A novel approach based on machine learning methods is presented in this proceedings. |
| Copyright/License | publication: (License: CC-BY-3.0) |
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